System and method for automated flight plan reporting in an electric aircraft

ABSTRACT

A system for automated flight plan reporting for an electric aircraft, the system including a flight controller coupled to the electric aircraft configured to receive a digital datum from a remote device, generate a plan adjustment datum as a function of the digital datum, and transmit the plan adjustment datum to a pilot display, a pilot display coupled to the electric aircraft, wherein the pilot display is configured to receive the plan adjustment datum from the flight controller, display the plan adjustment datum to a user; and receive a confirmation datum from the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Nonprovisional application Ser.No. 17/406,912, filed on Aug. 19, 2021, and entitled “SYSTEM AND METHODFOR AUTOMATED FLIGHT PLAN REPORTING IN AN ELECTRIC AIRCRAFT,” theentirety of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircraft. In particular, the present invention is directed to systemsand methods for automated flight plan reporting in an electric aircraft.

BACKGROUND

An air traffic control tower may have to route thousands of aircraftseach day, with little to no margin of error allowed. Having data relatedto an electric aircraft's flight plan that is automatically updated,while being properly authenticated, may be helpful for ensuring safe andefficient routing of aircrafts.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for automated flight plan reporting for anelectric aircraft, the system including a computing device configured tobe located within the electric aircraft, wherein the flight controlleris configured to receive a digital datum from a remote device, generatea plan adjustment datum as a function of the digital datum, and transmitthe plan adjustment datum to a pilot display. The system furtherincluding a pilot display communicatively connected to the electricaircraft, wherein the pilot display is configured to receive the planadjustment datum from the computing device, display the plan adjustmentdatum to a user, and receive a confirmation datum from the user.

In another aspect, a method for automated flight plan reporting in anelectric aircraft, the method including receiving, by a computing devicelocated within an electric aircraft, a digital datum from a remotedevice, generating, by the computing device, a plan adjustment datum asa function of the digital datum, transmitting, by the computing device,the plan adjustment datum to a pilot display, receiving, by a pilotdisplay communicatively connected to the electric aircraft, the planadjustment datum from the computing device, displaying, at the pilotdisplay, the plan adjustment datum to a user; and receiving, at thepilot display, a confirmation datum from the user.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is an illustrative block diagram of a system for automated flightplan reporting in an electric aircraft;

FIG. 2 is an exemplary flow diagram of a method for automated flightplan reporting in an electric aircraft;

FIG. 3A is an exemplary diagram of a brokered authentication system;

FIG. 3B is an exemplary representation of a brokered authenticationprocess

FIG. 4 an illustrative representation of flight controller;

FIG. 5 is a diagrammatic illustration of a machine learning process;

FIG. 6 is an exemplary embodiment of an electric aircraft;

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for automated flight plan reporting in an electricaircraft. In an embodiment, a flight controller coupled to the electricaircraft receives a flight plan datum from a remote device, such as anair traffic control tower, automatically, the flight controller alsotransmits a flight plan datum to a computing device, such as a pilotdisplay inside the aircraft, the pilot display receives the flight plandatum and displays it to a user.

Aspects of the present disclosure can be used to automatically updateinformation related to an aircraft's flight plan, and any changes madeto that plan, such as the aircraft deviating from a set flight plan or auser making changes to the set flight plan. Aspects of the presentdisclosure can also be used to transmit and receive flight plan datumthrough a digital radio frequency. This is so, at least in part, becausethe system is configured to communicate with remote devices usingdigital radio standard.

Aspects of the present disclosure allow for a brokered authenticationsystem, where an authentication broker may be at a recharging pad, suchas a server in the recharging pad. Exemplary embodiments illustratingaspects of the present disclosure are described below in the context ofseveral specific examples.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 forautomated flight plan reporting in an electric aircraft is illustrated.System 100 includes a flight controller 104. Flight controller 104 mayinclude any computing device as described in this disclosure, includingwithout limitation a microcontroller, microprocessor, digital signalprocessor (DSP) and/or system on a chip (SoC) as described in thisdisclosure. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. Flight controller 104 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. Flight controller 104 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Flightcontroller 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Flight controller 104 may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. Flight controller 104 may distribute oneor more computing tasks as described below across a plurality ofcomputing devices of computing device, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices. Flight controller 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or flight controller 104.

With continued reference to FIG. 1 , flight controller 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, flightcontroller 104 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Flight controller 104 may perform any step or sequence of stepsas described in this disclosure in parallel, such as simultaneouslyand/or substantially simultaneously performing a step two or more timesusing two or more parallel threads, processor cores, or the like;division of tasks between parallel threads and/or processes may beperformed according to any protocol suitable for division of tasksbetween iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Continuing to refer to FIG. 1 , flight controller 104 is configured toreceive a digital datum from a remote device 108. In one embodiment, theremote device may be a recharging pad. In some embodiments, the remotedevice may be an air traffic control tower. In some embodiments, theremote device may be a device in another aircraft. The remote device 108may include any computing device communicatively connected to the flightcontroller 104, and/or an authentication broker. A “digital datum”, asused herein for the purposes of this disclosure, refers to at least anelement of data identifying a command to change a current flight plan ofthe aircraft, such as a command to increase altitude, that istransmitted in digital form, such as sound transmission to a digitalradio. “Flight plan”, for the purpose of this disclosure, refers to theoptimum set of maneuvers, or commands, to be performed by the automatedelectric aircraft in order to reach a set objective. Flight plan may beconsistent with disclosure of flight plan in U.S. patent applicationSer. No. 17/365,512 and titled “PILOT-CONTROLLED POSITION GUIDANCE FORVTOL AIRCRAFT”, which is incorporated herein by reference in itsentirety. In an embodiment, the digital datum may be encrypted. In someembodiments, the digital datum may be encrypted using asymmetric keyencryption. In some embodiments, flight controller 104 is configured todecrypt the digital datum. In some embodiments, remote device may be acharging pad. In embodiments, remote device 108 may include a computingdevice operated by a fleet operator. In embodiments, remote device 108may include air traffic control. In embodiments, remote device 108 mayinclude a computing device operated by an air traffic controller.

Encryption process may involve the use of a datum, known as an“encryption key,” to alter plaintext. Cryptographic system may alsoconvert cyphertext back into plaintext, which is a process known as“decryption.” Decryption process may involve the use of a datum, knownas a “decryption key,” to return the cyphertext to its originalplaintext form. In embodiments of cryptographic systems that are“symmetric,” decryption key is essentially the same as encryption key:possession of either key makes it possible to deduce the other keyquickly without further secret knowledge. Encryption and decryption keysin symmetric cryptographic systems may be kept secret and shared onlywith persons or entities that the user of the cryptographic systemwishes to be able to decrypt the cyphertext. One example of a symmetriccryptographic system is the Advanced Encryption Standard (“AES”), whicharranges plaintext into matrices and then modifies the matrices throughrepeated permutations and arithmetic operations with an encryption key.

Still referring to FIG. 1 . In embodiments of cryptographic systems thatare “asymmetric,” either encryption or decryption key cannot be readilydeduced without additional secret knowledge, even given the possessionof a corresponding decryption or encryption key, respectively; a commonexample is a “public key cryptographic system,” in which possession ofthe encryption key does not make it practically feasible to deduce thedecryption key, so that the encryption key may safely be made availableto the public. An example of a public key cryptographic system is RSA,in which an encryption key involves the use of numbers that are productsof very large prime numbers, but a decryption key involves the use ofthose very large prime numbers, such that deducing the decryption keyfrom the encryption key requires the practically infeasible task ofcomputing the prime factors of a number which is the product of two verylarge prime numbers. Another example is elliptic curve cryptography,which relies on the fact that given two points P and Q on an ellipticcurve over a finite field, and a definition for addition where A+B=R,the point where a line connecting point A and point B intersects theelliptic curve, where “0,” the identity, is a point at infinity in aprojective plane containing the elliptic curve, finding a number k suchthat adding P to itself k times results in Q is computationallyimpractical, given correctly selected elliptic curve, finite field, andP and Q.

Still referring to FIG. 1 . Some embodiments of the disclosed systemsand methods involve creation and/or evaluation of digital signatures. Adigital signature as used herein is a secure proof performed on anelement of data, referred to as a “message”; secure proof may includeany secure proof as described in this disclosure. Message may includewithout limitation an encrypted mathematical representation of a file orother set of data. File or set of data may confer credentials, which maydemonstrate, without limitation, any result of any authentication orauthorization process performed by a signing device. Credentials, whichmay take the form of authorization tokens, may be generated, conferred,signed, or otherwise manipulated in any suitable way. Secure proof maybe enacted, without limitation, by encrypting message using a privatekey of a public key cryptographic system. Signature may be verified bydecrypting the encrypted mathematical representation using thecorresponding public key and comparing the decrypted representation to apurported match that was not encrypted; if the signature protocol iswell-designed and implemented correctly, this means the ability tocreate the digital signature is equivalent to possession of the privatedecryption key. Likewise, if mathematical representation of file iswell-designed and implemented correctly, any alteration of the file willresult in a mismatch with the digital signature; the mathematicalrepresentation may be produced using an alteration-sensitive, reliablyreproducible algorithm, such as a hashing algorithm as described infurther detail below. A mathematical representation to which thesignature may be compared may be included with signature, forverification purposes; in other embodiments, the algorithm used toproduce the mathematical representation is publicly available,permitting the easy reproduction of the mathematical representationcorresponding to any file.

Still referring to FIG. 1 . In some embodiments, persons, devices, ortransactions may be authenticated or assigned a confidence level usingdigital certificates and/or cryptographic keys. In one embodiment, adigital certificate is a file that conveys information and links theconveyed information to a “certificate authority” that is the issuer ofa public key or manager of public keys issued by other entities in apublic key cryptographic system. Certificate authority in someembodiments contains data conveying the certificate authority'sauthorization for the recipient to perform a task. The authorization maybe the authorization to access a given datum. The authorization may bethe authorization to access a given process. In some embodiments, thecertificate may identify the certificate authority. The digitalcertificate may include a digital signature.

Continuing to refer to FIG. 1 . In some embodiments, a third party suchas a certificate authority (CA) is available to verify that thepossessor of the private key is a particular entity or member of aparticular group; thus, if the certificate authority may be trusted, andthe private key has not been stolen, the ability of an entity to producea digital signature confirms the identity or membership of the entityand links the file to the entity in a verifiable way. Digital signaturemay be incorporated in a digital certificate, which is a documentauthenticating the entity possessing the private key by authority of theissuing certificate authority and signed with a digital signaturecreated with that private key and a mathematical representation of theremainder of the certificate. In other embodiments, digital signature isverified by comparing the digital signature to one known to have beencreated by the entity that purportedly signed the digital signature, ora member of the group to which the entity purportedly belongs; forinstance, if the public key that decrypts the known signature alsodecrypts the digital signature, where determining that decryption hasoccurred authentically may include an expected value, datum, etc., innon-limiting example in the case of a keyed-hash message authenticationcode (keyed HMAC) or hash-based authentication code (HMAC), the digitalsignature may be considered verified. Digital signature may also be usedto verify that the file has not been altered since the formation of thedigital signature.

Still referring to FIG. 1 , a certificate authority may include anauthentication broker that is configured to assign authenticationtokens. In embodiments, authentication broker may be connected to anidentity store to verify identity of entities being authenticated.Identity store may be a remote database, a certificate authority, adatabase locally coupled to the authentication broker, or any othersystem configured to store identification of devices and/or users. In anonlimiting example, authentication broker may be used by anorganization to assign security token within a closed authenticationecosystem. In another nonlimiting example, authentication broker may beincorporated in a charging pad, where devices authenticated locally andonly a local identity store is synched periodically with a remotedatabase.

In an embodiment, a certificate authority may include a manufacturer ofa device. For instance, manufacturer may verify that a private key, orsecret usable to produce a secure proof as set forth in further detailbelow, available to a device is associated with one or more devicesproduced by the manufacturer; verification may take the form of adigital signature created by the manufacturer, signing a verificationdatum and/or public key corresponding to the private key and/or secretbelonging to the device. Verification may be performed, withoutlimitation, by physically and/or electrically validating a silicon dieor other physical circuit element, for instance via automated testequipment, a probe station or other direct or indirect means ofinterrogating a circuit to validate that it is authenticallymanufactured, provides a particular expected behavioral result, or thelike; such verification may, for instance, permit a manufacturer todetermine that a sub-component or assembly manufactured by another partyin a supply chain was constructed authentically according to a design ofthe manufacturer. Verification may additionally or alternatively beperformed by reliance upon the trustworthiness of a subcomponentmanufacturer, and/or utilizing a cryptographic attestation result ofsuch a subcomponent. In non-limiting example, a device integrating anIntel SGX-enabled processor or other hardware or software deviceconfigured to perform an attestation of credentials may configure theprocessor to perform an attestation to the processor's manufacturer,make a determination as to the trustworthiness of the processor basedupon the attestation result, utilize this determination on the result asevidence of correct construction, and sign a verification datum and/orpubic key corresponding to the private key and/or secret belonging tothe device.

Still referring to FIG. 1 . Private key and/or secret may bedevice-specific or may be issued to a group of devices; in the lattercase, a device signing with private key and/or generating a secure proofbased on secret may convey that it belongs to that group of devices,without making it possible for a device evaluating the signature and/orsecure proof to identify the specific device other than as a member ofthe group. A secure proof, as used herein, is a protocol whereby anoutput is generated that demonstrates possession of a secret, such as asecret stored in or produced by originating device, withoutdemonstrating the entirety of the secret; in other words, a secure proofby itself, is insufficient to reconstruct the entire secret, enablingthe production of at least another secure proof using at least a secret.Where at least a secret is a plurality of secrets, such as a pluralityof challenge-response pairs, a secure proof may include an output thatreveals the entirety of one of the plurality of secrets, but not all ofthe plurality of secrets; for instance, secure proof may be a responsecontained in one challenge-response pair. A group of devices soidentified may have characteristics in common, such as instances and/orversions of hardware, firmware, or other elements, including withoutlimitation secure computing modules as described in further detailbelow; identification that device is in group may, for instance,indicate that device may be trusted to a certain degree. Groupmembership, attestation, and/or inclusion in an attestation chain, forinstance as described in this disclosure, may be specific to a piece ofsoftware running on a specific device, or to a hardware protected securecontainer (an enclave), such that any code executed within the enclavecan be assumed to be executed as its instruction set dictates upon thatinstruction set being loaded into memory, or otherwise processed and/ortransmitted via attestation chains, or be granted a certain confidencelevel, by virtue of degrees to which its secure computing module may betrusted to perform authentic attested processes or the like.Manufacturer and/or devices participating in embodiments of systems asdescribed herein may receive, assign, or generate confidence levelsassociated with such group identities, including without limitationassignment of lower or minimal confidence levels for groups with regardto which a vulnerability to hacking or exploits has been identified, agroup one member of which has been associated with an illegal,fraudulent, or undesirable act, a group one member of which has beencompromised, hacked, or stolen, or the like. In an embodiment, where atleast a member of a group has been compromised, given a lower confidencelevel, or the like, individual devices in group having device-specificsecrets or private keys may sign transactions with such keys,sacrificing a degree of anonymity to differentiate themselves fromcompromised members of such groups. Alternatively, or additionally, averifier of an attestation may require an attesting device to provideevidence that it is not one of a particular set of devices believed tohave been compromised or otherwise revoked. Practically this list ofmalicious devices may include a list of suspected signatures created bysuch devices, rather than the device private key; proof ofnon-membership may include provision of proof that the attesting devicecould not have generated the same signature. In either case, attestingdevice may generate a proof of innocence by proving membership in anhonest set (e.g., providing a datum signed by the device's private keyor other persistent unique identifier to an authority). In analternative embodiment, either as part of the algorithm determining theauthenticity of a device or device-and-software component and assigningor refreshing certificates or credentials conferring some trustedstatus, and/or after initial assignment of certificates, a device isrequired to prove to an authority that it is not a member of a dishonestset (e.g. is not a device suspected of compromise), the compromiseddevice being identified by an example of a signature created by saiddevice or some other means. In such an example, the element wishing toprove innocence generates a proof that it could not have generated theexample of a compromised signature.

In an embodiment, a protocol may be implemented that modifies aparticular element of the credential issued to a device (a credentialthat confers in that device a device-specific, device-and-softwarespecific, or set of one or more of the preceding) at some interval oftime, that interval being fixed or variable. A result may be that atleast the issuer or set of issuers may make a determination based on thecredential issued to an element or the signature of said element as toin what interval of time, or time epoch, the credential was issued.Further, protocol may incorporate an algorithm that outputs a proof of agiven device's innocence by either of the above means or in combination,the input of the algorithm being the set of suspected compromisedsignatures from the same time epoch. In contrast to existing approaches,a proof of innocence is specific to the set signatures from suspectedcompromised devices whose credentials were issued in the same window oftime as the device providing the proof. Thus, rather than requiring thata device prove that it has not made any of the suspected compromisedsignatures over all time, the device may only be required to provenon-membership in or membership in a much smaller group, conferringsignificant performance benefit.

Continuing to refer to FIG. 1 . Group keys and/or secrets may beembedded in hardware or software of devices during manufacture, asdescribed in further detail below. Group keys and/or secrets may beassigned and/or signed by devices other than manufacturers; group keysand/or assignment of group keys may be used in direct anonymousattestation as described in further detail below. Group keys may enableprocesses of identification and/or attestation, such as withoutlimitation direct anonymous attestation, to be performed in which adigital signature and/or secure proof confirms that an entity and/ordevice is part of a group but cannot determine which member of a groupit is.

Still referring to FIG. 1 . In other embodiments, for instance asdescribed in further detail below, where trust in a single certificateauthority is undesirable (e.g., where there is concern of thecertificate authority and verifier colluding, or there is concern that asingle certificate authority may be vulnerable to attack), the samefunctionality may be accomplished by a group of certificate authoritiesacting to authenticate in coordination, with the requirement that athreshold number of the group of certificate authorities (e.g. viamulti-signature), and/or a threshold proportion of the group ofcertificate authorities, agree (e.g. “threshold cryptography”); aconfidence level in each certificate authority may be determinedaccording to any method or means described herein for determination of aconfidence level in any device or entity, including without limitationin a cryptographic evaluator as described in further detail below. In anembodiment, certificate authorities that have a confidence level below agiven threshold level may be eliminated; in other embodiments,certificate authority confidence levels may be aggregated according toany method shown herein. Additional embodiments may include groupsignature schemes that issue certificates on a membership public keygenerated by a secure computing module as described in further detailbelow; in such scenarios, authentication may include proof by the securecomputing module that the secure computing module possesses a secret keyto a public key/certificate pair. Although digital signatures have beenintroduced here as performed using public key cryptographic systems,digital signatures may alternatively or additionally be performed usingsymmetric cryptography and/or any interactive or non-interactivezero-knowledge proof; for instance, a proof may be recorded inconjunction with a datum, and a verification may be performed by anyparty seeking to evaluate the proof

Still referring to FIG. 1 . In some embodiments, systems and methodsdescribed herein produce cryptographic hashes, also referred to by theequivalent shorthand term “hashes.” A cryptographic hash, as usedherein, is a mathematical representation of a lot of data, such as filesor blocks in a block chain as described in further detail below; themathematical representation is produced by a lossy “one-way” algorithmknown as a “hashing algorithm.” Hashing algorithm may be a repeatableprocess, that is, identical lots of data may produce identical hasheseach time they are subjected to a particular hashing algorithm. Becausehashing algorithm is a one-way function, it may be impossible toreconstruct a lot of data from a hash produced from the lot of datausing the hashing algorithm. In the case of some hashing algorithms,reconstructing the full lot of data from the corresponding hash using apartial set of data from the full lot of data may be possible only byrepeatedly guessing at the remaining data and repeating the hashingalgorithm; it is thus computationally difficult if not infeasible for asingle computer to produce the sheer amount of data, as the statisticallikelihood of correctly guessing the missing data may be extremely low.However, the statistical likelihood of a computer of a set of computerssimultaneously attempting to guess the missing data within a usefultimeframe may be higher, permitting mining protocols as described infurther detail below.

Continuing to refer to FIG. 1 . In an embodiment, hashing algorithm maydemonstrate an “avalanche effect,” whereby even extremely small changesto lot of data produce drastically different hashes. This may thwartattempts to avoid the computational work necessary to recreate a hash bysimply inserting a fraudulent datum in data lot, enabling the use ofhashing algorithms for “tamper-proofing” data such as data contained inan immutable ledger as described in further detail below. This avalancheor “cascade” effect may be evinced by various hashing processes; personsskilled in the art, upon reading the entirety of this disclosure, willbe aware of various suitable hashing algorithms for purposes describedherein. Verification of a hash corresponding to a lot of data may beperformed by running the sheer amount of data through a hashingalgorithm used to produce the hash. Such verification may becomputationally expensive, albeit feasible, potentially adding up tosignificant processing delays where repeated hashing, or hashing oflarge quantities of data, is required, for instance as described infurther detail below. Examples of hashing programs include, withoutlimitation, SHA256, a NIST standard; further current and past hashingalgorithms include Winternitz hashing algorithms, various generations ofSecure Hash Algorithm (including “SHA-1,” “SHA-2,” and “SHA-3”),“Message Digest” family hashes such as “MD4,” “MD5,” “MD6,” and“RIPEMD,” Keccak, “BLAKE” hashes and progeny (e.g., “BLAKE2,”“BLAKE-256,” “BLAKE-512,” and the like), Message Authentication Code(“MAC”)-family hash functions such as PMAC, OMAC, VMAC, HMAC, and UMAC,Polyl305-AES, Elliptic Curve Only Hash (“ECOH”) and similar hashfunctions, Fast-Syndrome-based (FSB) hash functions, GOST hashfunctions, the Gr∅stl hash function, the HAS-160 hash function, the JHhash function, the RadioGatún hash function, the Skein hash function,the Streebog hash function, the SWIFFT hash function, the Tiger hashfunction, the Whirlpool hash function, or any hash function thatsatisfies, at the time of implementation, the requirements that acryptographic hash be deterministic, infeasible to reverse-hash,infeasible to find collisions, and have the property that small changesto an original message to be hashed will change the resulting hash soextensively that the original hash and the new hash appear uncorrelatedto each other. A degree of security of a hash function in practice maydepend both on the hash function itself and on characteristics of themessage and/or digest used in the hash function. For example, where amessage is random, for a hash function that fulfillscollision-resistance requirements, a brute-force or “birthday attack”may to detect collision may be on the order of 0(2n/2) for n outputbits; thus, it may take on the order of 2256 operations to locate acollision in a 512 bit output “Dictionary” attacks on hashes likely tohave been generated from a non-random original text can have a lowercomputational complexity, because the space of entries they are guessingis far smaller than the space containing all random permutations ofbits. However, the space of possible messages may be augmented byincreasing the length or potential length of a possible message, or byimplementing a protocol whereby one or more randomly selected strings orsets of data are added to the message, rendering a dictionary attacksignificantly less effective.

Additionally, or alternatively, and still referring to FIG. 1 , therecharging pad may include a landing pad, where the landing pad may beany designated area for the electric airplane to land and/or takeoff. Inone embodiment, the landing pad may be made of any suitable material andmay be any dimension. In some embodiments, the landing pad may be ahelideck or a helipad.

In some embodiments, and continuing to refer to FIG. 1 , the rechargingpad may include a recharging component coupled to the landing pad, wherethe recharging component may include any component with the capabilityof recharging an energy source, such as a battery, of the electricairplane. In one embodiment, the recharging component may include aconstant voltage charger, a constant current charger, a taper currentcharger, a pulser charger, a negative pulse charger, an IUI charger, atrickle charger, a float charger, a random charger, and the like.

In one embodiment, and still referring to FIG. 1 , recharging pad mayinclude a support component coupled to the bottom of the landing pad,where the support component may include any space dedicated forsupporting the electric aircraft. In some embodiments, the supportcomponent may include an area dedicated to storage, a workshop foraircraft maintenance, an area dedicated to logistics, a pilot lounge,sleeping accommodations, a generator, and the like. In a nonlimitingexample, the flight pad is a raised platform that is wide enough for aneVTOL to land on it, furnished with a charging dock and with acompartment under the landing platform where the pilot may rest, orequipment related to electric vehicle charging may be stored.

Still referring to FIG. 1 . In one embodiment, the flight controller 104may be configured to receive the digital datum using a digital radiostandard. In an embodiment, the digital radio standard may include,without limitations, a JTRS (Joint Tactical System), a SINCGARS (Singlechannel ground to air radio system), and the like. In embodiments,flight controller 104 may include a digital radio receiver. Inembodiments, flight controller 104 is communicatively coupled to adigital radio receiver. In embodiments, flight controller 104 may beconfigured to transform an analog radio transmission into the digitaldatum. In embodiments, flight controller 104 may be configured toreceive the digital datum through a mobile wireless network. Nonlimitingexamples of wireless standards used by flight controller 104 may include1G, 2G, 3G, 4G and 5G. In embodiments, flight controller 104 may beconfigured to receive digital datum through a mobile satellitecommunications and mobile satellite services (MMS). In nonlimitingexamples, MMS communication may utilize Geostationary Orbit (GEO)satellites, Medium Earth Orbit (MEO) satellites, Low Earth Orbit (LEO)satellites, and the like. In a nonlimiting example, ground control maybroadcast commands that may cause a change in a flight plan through aSINCGARS standard. In embodiments, flight controller 104 may beconfigured to receive the digital datum from a flight operator, such assending a command to reduce speed as to conserve battery. Inembodiments, flight controller 104 may be configured to authentication aconnection with the remote device 108 before receiving the digitaldatum. In embodiments, flight controller 104 may utilize anauthentication broker to authenticate the connection. In embodiments,digital datum may include authentication data, such as a deviceidentifier. In an embodiment, digital datum may include anauthentication token identifying the remote device. In embodiments,flight controller 104 may be configured to send a digital datumrequesting authentication.

Continuing to refer to FIG. 1 , flight controller 104 is configured togenerate a plan adjustment datum as a function of the digital datum.“Plan adjustment datum” refers to elements of data that differs from thepreviously generated flight plan, such as a change in flight directionor change. In a nonlimiting example, the plan adjustment datum mayinclude information related to the changes required to be made to theflight plan as to avoid a collision with another aircraft.

Still referring to FIG. 1 , flight controller is configured to transmitthe plan adjustment datum to a pilot display 112. In an embodiment, thepilot display 112 is communicatively connected to the flight controller104. In embodiments, the remote device 108 includes the pilot display112. In a nonlimiting example, pilot display 112 may include asmartphone operated by a fleet operator. In another nonlimiting example,the pilot display 112 may be a device operated by an air trafficcontroller.

With continued reference to FIG. 1 , system 100 includes a pilot display116 where the pilot display 116 is configured to receive the planadjustment datum from the flight controller 104. In an embodiment, thepilot display 116 is configured to decrypt an encrypted plan adjustmentdatum. A “pilot display”, as used herein for the purpose of thisdisclosure, is an output device for presentation of information invisual or tactile form. As a non-limiting example, a display device mayinclude liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, an electroluminescent(ELD) display, a quantum dot (QLED) display, and the like thereof in anycombination. In an embodiment, and without limitation, pilot display 112may include a primary flight display, multi-function display, and thelike thereof. The pilot display 112 may include any computing deviceconfigured to display data such as a laptop, desktop, smartphone,computing tablet, smartwatch, and the like. Pilot display 112 isconfigured to present, to a user, information related to the flightplan. Pilot display 116 may include a graphical user interface,multi-function display (MFD), primary display, gauges, graphs, audiocues, visual cues, information on a heads-up display (HUD) or acombination thereof. Pilot display 112 may include a display disposed inone or more areas of an aircraft, on a user device remotely located, oneor more computing devices 112, or a combination thereof. Pilot display112 may be disposed in a projection, hologram, or screen within a user'shelmet, eyeglasses, contact lens, or a combination thereof. Pilotdisplay 112 may display the flight plan in graphical form. Graphicalform may include a two-dimensional plot of two variables that representdata received by the flight controller 104, such as past flighttrajectory and future flight plan. In one embodiment, pilot display 112may also display the user's input in real-time. In a nonlimitingexample, the pilot display 116 may display a two-dimensional plot withthe past flight trajectory and the future planned flight trajectory.

Still referring to FIG. 1 , pilot display 112 is further configured todisplay the plan adjustment datum to a user. Pilot display 112 isfurther configured to receive a confirmation datum from the user. In anembodiment, the pilot display 112 may be further configured to transmitthe confirmation datum as a function of an input by the user.“Confirmation datum” refers to an element of data confirming the planadjustment datum. In an embodiment, flight controller 104 may beconfigured to make changes to the flight plan as a function of theconfirmation datum received from the user. In an embodiment, pilotdisplay 112 may be further configured to encrypt the confirmation datum.In a nonlimiting example, a user may visualize a future flight planthrough a smartphone. In another nonlimiting example, a fleet operatormay visualize the past flight and current trajectory of an electricaircraft on a laptop. In some embodiments, the flight controller 104 maybe configured to automatically transmit a change in the flight plan to aremote device 108.

Now referring to FIG. 2 , an exemplary method 200 for automated flightplan reporting in an electric aircraft is illustrated. At step 205, themethod 200 includes receiving, by the flight controller 104, a digitaldatum from a remote device 108. In an embodiment, method may furtherinclude receiving, by the flight controller 104, the digital datum as afunction of a remote device. In embodiments, remote device may be anauthentication broker. In a nonlimiting example, an air traffic controldevice may send a command to change its direction to a specifiedcoordinates as to avoid another aircraft that has being routed throughthe electric aircraft's flight path described in its flight plan. Inanother nonlimiting example, flight controller 104 may authenticate theremote device sending the digital datum by using an authenticationbroker.

Still referring to FIG. 2 , at step 210, method 200 includes generating,by the flight controller 104, a plan adjustment datum as a function ofthe digital datum. In a nonlimiting example, flight controller receivescommands related to its flight from ground control and generatesinformation related to the changes that will be effected on the setflight plan based on those commands, such as estimated total flight timebased on a command to reduce speed relayed by ground control. Continuingto refer to FIG. 2 , at step 215, method 200 includes transmitting, bythe flight controller 104, the plan adjustment datum to a pilot display112. In a nonlimiting example, plan adjustment datum is sent over acable connection when pilot display is locally connected to the flightcontroller 104, such as a display inside the electric vehicle's cockpit.In another nonlimiting example, flight controller 104 transmits the planadjustment datum over a satellite connection. In embodiments, method mayinclude transmitting, by the flight controller, a credential to abroker; and receiving, by the flight controller, a token from thebroker. In embodiments, method 200 may further include transmitting, bythe flight controller, the token to the remote device as a function ofthe confirmation datum. In embodiments, method 200 may further includetransmitting, by the flight controller, the plan adjustment datum usinga digital radio standard.

Continuing to refer to FIG. 2 , method 200, at step 220, includesreceiving, at the pilot display 112, the plan adjustment datum from theflight controller 104. In a nonlimiting example, the plan adjustmentdatum is received through a mobile network, such as 4G.

Still referring to FIG. 2 , at step 225, method 200 includes displaying,at the pilot display 112, the plan adjustment datum to a user. In anembodiment, original flight plan may also be displayed. In a nonlimitingexample, pilot display 112 may display a graph representing the originalflight path and a graph representing the new flight path based on theplan adjustment datum. In a nonlimiting example, pilot display 112 maydisplay graphical representation of the changes to the flight plan basedon the plan adjustment datum, where the changes are graphicallypresented being superimposed on top of the original flight plan.

Still referring to FIG. 2 , method 200, at step 235, includes receiving,at the pilot display 112, a confirmation datum from the user. Inembodiments, the confirmation datum may be a voice command. Inembodiments, confirmation datum may be a click of a button. In anonlimiting example, confirmation datum may be generated by a userclicking a “confirm changes” dialog box displayed through a touchscreendisplay. In embodiments, method may further include transmitting, by theflight controller, the plan adjustment datum to the remote device as afunction of the confirmation datum. In embodiments, method 200 mayfurther include receiving the confirmation datum, at the flightcontroller. In embodiments, method 200 may include storing, by theflight controller, the digital datum in a database; and storing, by theflight controller, the plan adjustment datum in the database as afunction of the confirmation datum

Now referring to FIG. 3A, an exemplary depiction of a brokeredauthentication 300 system is illustrated. In an embodiment, theauthentication broker 304 may be a charging pad. In a nonlimitingexample, the flight controller 104 makes a request for a confirmationdatum to a remote device 108, system 100 then redirects the flightcontroller 104 request to the authentication broker 304, theauthentication broker 304 then authenticate the flight controller 104against a central identity store, if the flight controller 104 issuccessfully authenticated, the authentication broker 304 assigns asecurity token to the flight controller 104 and redirects the flightcontroller 104 request to the remote device 108, the remote device thensends a validation request for the flight controller 104 to theauthentication broker 304, after validation is successful, the remotedevice 108 sends the flight confirmation datum to the flight controller104. The authentication broker 304 may be used, without limitation, inany brokered authentication system. Authentication broker 304 mayinclude any form of circuit suitable for use as any other componentdescribed herein, including without limitation an integrated circuitand/or a circuit configured using software, including without limitationkey ladders, authentication broker 304 is configured to produce at leastan output comprising proof of the module-specific secret.

Still referring to FIG. 3A. Authentication broker 304 may include asoftware-defined circuit; in other words, identity store 308 may includeand/or consist of a software program implemented on a component ofsecure computing hardware apparatus and/or a computing device in whichsecure computing hardware apparatus is incorporated. Identity store 308may include any means and/or components to create a cryptographicidentity within the authentication broker 304.

Referring now to FIG. 3B, an exemplary diagram of a brokeredauthentication 300 process is shown. In a nonlimiting example, flightcontroller 104 sends an authentication request to the authenticationbroker 304, authentication broker 304 then validates the flightcontroller's 104 credential against an identity store 308, if valid,identity store 308 sends a validation response to the authenticationbroker 304. The authentication broker 304 proceeds to send a successfulauthentication response, that may include an authentication token, tothe flight controller 104. Once validated by the authentication broker304, flight controller 104 sends a request to a remote device 108 for aconfirmation datum, the remote device 108 then service the request forthe confirmation datum. In some embodiments, the identity store 308 maybe a remote database. In some embodiments, the authentication broker 304is locally connected to the identity store 308.

Still referring to FIG. 3B. In an embodiment, identity store 308 mayselect and/or generate a device identifier, secure proof, and/orcryptographic identity at random every time authentication broker 304 isinvoked, at every boot and/or attested boot, or the like. A proof, asdescribed throughout this disclosure, may include any element of datathat demonstrates possession of the module-specific secret. Proof mayinclude a secure proof of the module-specific secret. A secure proof, asdescribed throughout this disclosure, may include a protocol whereby anoutput is generated that demonstrates possession of a secret, such asmodule-specific secret, without demonstrating the entirety of themodule-specific secret; in other words, a secure proof by itself, isinsufficient to reconstruct the entire module-specific secret, whichenables the production of at least another secure proof using at least amodule-specific secret while preventing spoofing or imitation byrecipients or third-party listeners. Where at least a module-specificsecret is a plurality of secrets, such as a plurality ofchallenge-response pairs, a secure proof may include an output thatreveals the entirety of one of the plurality of secrets, but not all ofthe plurality of secrets, for instance, secure proof may be a responsecontained in one challenge-response pair. In an embodiment, proof maynot be secure; in other words, proof may include a one-time revelationof at least a module-specific secret, for instance as used in a singlechallenge-response exchange.

Now referring to FIG. 4 , an exemplary embodiment 400 of a flightcontroller 404 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 404 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 404may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 404 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 4 , flight controller 404may include a signal transformation component 408. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 408 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component408 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 408 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 408 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 408 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof

Still referring to FIG. 4 , signal transformation component 408 may beconfigured to optimize an intermediate representation 412. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 408 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 408 may optimizeintermediate representation 412 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 408 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 408 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 404. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 408 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 4 , flight controller 404may include a reconfigurable hardware platform 416. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 416 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 4 , reconfigurable hardware platform 416 mayinclude a logic component 420. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 420 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 420 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 420 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 420 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 420 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 412. Logiccomponent 420 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 404. Logiccomponent 420 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 420 may beconfigured to execute the instruction on intermediate representation 412and/or output language. For example, and without limitation, logiccomponent 420 may be configured to execute an addition operation onintermediate representation 412 and/or output language.

In an embodiment, and without limitation, logic component 420 may beconfigured to calculate a flight element 424. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 424 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 424 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 424 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 4 , flight controller 404 may include a chipsetcomponent 428. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 428 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 420 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 428 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 420 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 428 maymanage data flow between logic component 420, memory cache, and a flightcomponent 432. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 432 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component432 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 428 may be configured to communicate witha plurality of flight components as a function of flight element 424.For example, and without limitation, chipset component 428 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 4 , flight controller 404may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 404 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 424. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 404 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 404 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 4 , flight controller 404may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 424 and a pilot signal436 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 436may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 436 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 436may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 436 may include an explicitsignal directing flight controller 404 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 436 may include an implicit signal, wherein flight controller 404detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 436 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 436 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 436 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 436 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal436 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 4 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 404 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 404.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naive bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 4 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 404 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 4 , flight controller 404 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 404. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 404 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, a autonomous machine-learning process correction, andthe like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 404 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 4 , flight controller 404 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 4 , flight controller 404may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller404 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 404 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 404 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Massachusetts, USA. In an embodiment, and withoutlimitation, control algorithm may be configured to generate anauto-code, wherein an “auto-code,” is used herein, is a code and/oralgorithm that is generated as a function of the one or more modelsand/or software's. In another embodiment, control algorithm may beconfigured to produce a segmented control algorithm. As used in thisdisclosure a “segmented control algorithm” is control algorithm that hasbeen separated and/or parsed into discrete sections. For example, andwithout limitation, segmented control algorithm may parse controlalgorithm into two or more segments, wherein each segment of controlalgorithm may be performed by one or more flight controllers operatingon distinct flight components.

In an embodiment, and still referring to FIG. 4 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 432. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 4 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 404. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 412 and/or output language from logiccomponent 420, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 4 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 4 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 4 , flight controller 404 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 404 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 4 , a node may include, without limitation aplurality of inputs xi that may receive numerical values from inputs toa neural network containing the node and/or from other nodes. Node mayperform a weighted sum of inputs using weights w_(i) that are multipliedby respective inputs x_(i). Additionally or alternatively, a bias b maybe added to the weighted sum of the inputs such that an offset is addedto each unit in the neural network layer that is independent of theinput to the layer. The weighted sum may then be input into a functionϕ, which may generate one or more outputs y. Weight w_(i) applied to aninput x_(i) may indicate whether the input is “excitatory,” indicatingthat it has strong influence on the one or more outputs y, for instanceby the corresponding weight having a large numerical value, and/or a“inhibitory,” indicating it has a weak effect influence on the one moreinputs y, for instance by the corresponding weight having a smallnumerical value. The values of weights w_(i) may be determined bytraining a neural network using training data, which may be performedusing any suitable process as described above. In an embodiment, andwithout limitation, a neural network may receive semantic units asinputs and output vectors representing such semantic units according toweights w_(i) that are derived using machine-learning processes asdescribed in this disclosure.

Still referring to FIG. 4 , flight controller may include asub-controller 440. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 404 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 440may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 440 may include any component of any flightcontroller as described above. Sub-controller 440 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 440may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 440 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 4 , flight controller may include aco-controller 444. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 404 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 444 mayinclude one or more controllers and/or components that are similar toflight controller 404. As a further non-limiting example, co-controller444 may include any controller and/or component that joins flightcontroller 404 to distributer flight controller. As a furthernon-limiting example, co-controller 444 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 404 to distributed flight control system. Co-controller 444may include any component of any flight controller as described above.Co-controller 444 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 4 , flightcontroller 404 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 404 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 5 , an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 504 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 508 given data provided as inputs 512;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 5 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 504 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 504 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 504 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 504 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 504 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 504 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data504 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5 ,training data 504 may include one or more elements that are notcategorized; that is, training data 504 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 504 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 504 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 504 used by machine-learning module 500 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 5 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 516. Training data classifier 516 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 5 , machine-learning module 500 may beconfigured to perform a lazy-learning process 520 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 504. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 504 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 5 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 504set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 5 , machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 528, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 504. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process528 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 5 , machine learning processes may include atleast an unsupervised machine-learning processes 532. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5 , machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naive Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 6 , an embodiment of an electric aircraft 600 ispresented. Still referring to FIG. 6 , electric aircraft 600 may includea vertical takeoff and landing aircraft (eVTOL). As used herein, avertical take-off and landing (eVTOL) aircraft is one that can hover,take off, and land vertically. An eVTOL, as used herein, is anelectrically powered aircraft typically using an energy source, of aplurality of energy sources to power the aircraft. In order to optimizethe power and energy necessary to propel the aircraft. eVTOL may becapable of rotor-based cruising flight, rotor-based takeoff, rotor-basedlanding, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Rotor-basedflight, as described herein, is where the aircraft generated lift andpropulsion by way of one or more powered rotors coupled with an engine,such as a “quad copter,” multi-rotor helicopter, or other vehicle thatmaintains its lift primarily using downward thrusting propulsors.Fixed-wing flight, as described herein, is where the aircraft is capableof flight using wings and/or foils that generate life caused by theaircraft's forward airspeed and the shape of the wings and/or foils,such as airplane-style flight.

With continued reference to FIG. 6 , a number of aerodynamic forces mayact upon the electric aircraft 600 during flight. Forces acting on anelectric aircraft 600 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 600 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 600 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 600 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 600 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 600 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 600 downward due to the force of gravity. Anadditional force acting on electric aircraft 600 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor of the electric aircraft. Liftgenerated by the airfoil may depend on speed of airflow, density of air,total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 600 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of an electric aircraft600, including without limitation propulsors and/or propulsionassemblies. In an embodiment, the motor may eliminate need for manyexternal structural features that otherwise might be needed to join onecomponent to another component. The motor may also increase energyefficiency by enabling a lower physical propulsor profile, reducing dragand/or wind resistance. This may also increase durability by lesseningthe extent to which drag and/or wind resistance add to forces acting onelectric aircraft 600 and/or propulsors.

Still referring to FIG. 6 , electric aircraft 600 may include a flightcontroller 104, where the flight controller may be communicativelyconnected to a computing device 112, such as a pilot display 116. Insome embodiments, electric aircraft 600 may a pilot display 116 insidethe cockpit.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for automated flight plan reporting foran electric aircraft, the system comprising: a computing deviceconfigured to be located within the electric aircraft, wherein theflight controller is configured to: receive a digital datum from aremote device; generate a plan adjustment datum as a function of thedigital datum; and transmit the plan adjustment datum to a pilotdisplay; a pilot display communicatively connected to the electricaircraft, wherein the pilot display is configured to: receive the planadjustment datum from the computing device; display the plan adjustmentdatum to a user; and receive a confirmation datum from the user.
 2. Thesystem of claim 1, wherein the plan adjustment datum comprises a changein flight direction of the electric aircraft.
 3. The system of claim 1,wherein receiving the plan adjustment datum comprises decrypting anencrypted plan adjustment datum.
 4. The system of claim 1, wherein thepilot display is further configured to transmit the confirmation datumas a function of an input by the user.
 5. The system of claim 1, whereinthe computing device is further configured to: transmit a credential toan authentication broker; and receive a token from the authenticationbroker.
 6. The system of claim 5, wherein the computing device isfurther configured to transmit the token to the remote device as afunction of the confirmation datum.
 7. The system of claim 1, whereinthe authentication broker is incorporated in a recharging pad.
 8. Thesystem of claim 1, wherein the remote device is a flight managementdevice.
 9. The system of claim 1, wherein the computing device isfurther configured to: store the digital datum in a database; and storethe plan adjustment datum in the database as a function of theconfirmation datum.
 10. The system of claim 1, wherein the computingdevice comprises a mobile device.
 11. A method for automated flight planreporting in an electric aircraft, the method comprising: receiving, bya computing device located within an electric aircraft, a digital datumfrom a remote device; generating, by the computing device, a planadjustment datum as a function of the digital datum; transmitting, bythe computing device, the plan adjustment datum to a pilot display;receiving, by a pilot display communicatively connected to the electricaircraft, the plan adjustment datum from the computing device;displaying, at the pilot display, the plan adjustment datum to a user;and receiving, at the pilot display, a confirmation datum from the user.12. The method of claim 11, wherein the plan adjustment datum comprisesa change in flight direction of the electric aircraft.
 13. The method ofclaim 11, wherein receiving the plan adjustment datum comprisesdecrypting an encrypted plan adjustment datum.
 14. The method of claim11, further comprising, transmitting, by the pilot display, theconfirmation datum as a function of an input by the user.
 15. The methodof claim 11, further comprising: Transmitting, by the computing device,a credential to an authentication broker; and Receiving, by thecomputing device, a token from the authentication broker.
 16. The methodof claim 15, further comprising transmitting, by the computing device,the token to the remote device as a function of the confirmation datum.17. The method of claim 11, wherein the authentication broker isincorporated in a recharging pad.
 18. The method of claim 11, whereinthe remote device is a flight management device.
 19. The method of claim11, further comprising: storing, by the computing device, the digitaldatum in a database; and storing, by the computing device, the planadjustment datum in the database as a function of the confirmationdatum.
 20. The method of claim 11, wherein the computing devicecomprises a mobile device.